RAG-Enhanced LLM System
(Redirected from retrieval-augmented generation system)
Jump to navigation
Jump to search
A RAG-Enhanced LLM System is a hybrid knowledge-augmented LLM-based system that can support RAG-enhanced LLM tasks (by combining retrieval mechanisms with generation capability).
- AKA: Retrieval-Augmented Generation System, RAG LLM System, Knowledge-Grounded LLM System.
- Context:
- It can typically implement RAG-Enhanced Retrieval Components through RAG-enhanced dense retrieval, RAG-enhanced sparse retrieval, and RAG-enhanced hybrid search.
- It can typically utilize RAG-Enhanced Embedding Models through RAG-enhanced text encoders, RAG-enhanced similarity metrics, and RAG-enhanced vector indexes.
- It can typically manage RAG-Enhanced Knowledge Bases through RAG-enhanced document stores, RAG-enhanced chunk management, and RAG-enhanced metadata handling.
- It can typically perform RAG-Enhanced Context Integrations through RAG-enhanced prompt augmentation, RAG-enhanced context windows, and RAG-enhanced relevance scoring.
- It can typically enable RAG-Enhanced Generation Controls through RAG-enhanced source attribution, RAG-enhanced factual grounding, and RAG-enhanced hallucination reduction.
- ...
- It can often support RAG-Enhanced Multi-Hop Reasonings through RAG-enhanced iterative retrieval, RAG-enhanced chain reasoning, and RAG-enhanced evidence aggregation.
- It can often facilitate RAG-Enhanced Real-Time Updates through RAG-enhanced index refresh, RAG-enhanced incremental learning, and RAG-enhanced dynamic knowledge.
- It can often implement RAG-Enhanced Cross-Modal Retrievals through RAG-enhanced image search, RAG-enhanced video retrieval, and RAG-enhanced multimodal fusion.
- It can often provide RAG-Enhanced Personalizations through RAG-enhanced user profiles, RAG-enhanced preference learning, and RAG-enhanced contextual adaptation.
- ...
- It can range from being a Simple RAG-Enhanced LLM System to being a Advanced RAG-Enhanced LLM System, depending on its RAG-enhanced system complexity.
- It can range from being a Single-Source RAG-Enhanced LLM System to being a Multi-Source RAG-Enhanced LLM System, depending on its RAG-enhanced knowledge diversity.
- It can range from being a Static RAG-Enhanced LLM System to being a Dynamic RAG-Enhanced LLM System, depending on its RAG-enhanced update frequency.
- It can range from being a Domain-Specific RAG-Enhanced LLM System to being a General-Purpose RAG-Enhanced LLM System, depending on its RAG-enhanced application scope.
- ...
- It can integrate with RAG-Enhanced Vector Databases for RAG-enhanced similarity search, RAG-enhanced index management, and RAG-enhanced query optimization.
- It can connect to RAG-Enhanced Document Processors for RAG-enhanced text extraction, RAG-enhanced chunking strategy, and RAG-enhanced preprocessing.
- It can interface with RAG-Enhanced Reranking Models for RAG-enhanced relevance refinement, RAG-enhanced result filtering, and RAG-enhanced quality scoring.
- It can communicate with RAG-Enhanced Cache Systems for RAG-enhanced response caching, RAG-enhanced retrieval optimization, and RAG-enhanced latency reduction.
- It can synchronize with RAG-Enhanced Monitoring Platforms for RAG-enhanced performance tracking, RAG-enhanced quality metrics, and RAG-enhanced usage analytics.
- ...
- Example(s):
- Enterprise RAG-Enhanced LLM Systems, such as:
- Customer Support RAG-Enhanced Systems, such as:
- Knowledge Management RAG-Enhanced Systems, such as:
- Healthcare RAG-Enhanced LLM Systems, such as:
- Clinical Decision RAG-Enhanced Systems, such as:
- Medical Research RAG-Enhanced Systems, such as:
- Legal RAG-Enhanced LLM Systems, such as:
- ...
- Enterprise RAG-Enhanced LLM Systems, such as:
- Counter-Example(s):
- Standalone LLM Systems, which lack RAG-enhanced retrieval components and RAG-enhanced knowledge grounding.
- Pure Retrieval Systems, which lack RAG-enhanced generation capability and RAG-enhanced language understanding.
- Static Knowledge Base Systems, which lack RAG-enhanced dynamic retrieval and RAG-enhanced contextual generation.
- See: Retrieval-Augmented Natural Language Generation (RAG) Technique, Vector Database, Embedding Model, Dense Retrieval, Knowledge Grounding, Context Window, Hallucination Mitigation.